What /r/ Sounds Like in Kansai Japanese: A Phonetic Investigation of Liquid Variation in Unscripted Discourse
Bibliographic record
Abstract
Unlike Canadian English which has two liquid consonant phonemes, /ɹ, l/ (as in right and light), Japanese is said to have a single liquid phoneme whose realization varies widely both among speakers and within the speech of individuals. Although variants of the /r/ sound in Japanese have been described as flaps, laterals, and weak plosives, research that has sought to quantitatively describe this phonetic variation has not yet been carried out. The aim of this thesis is to provide such quantification based on 1,535 instances of /r/ spoken by four individuals whose near-natural, unscripted conversations had been recorded as part of a larger corpus of unscripted Japanese maintained by Dr. Nick Campbell of Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan. Tokens of /r/ were extracted from 30-minute conversations between one pair of male speakers and one pair of female speakers. Each token was narrowly transcribed into the International Phonetic Alphabet, then categorized based on the author’s perception of: 1) the strength/narrowness of central oral articulatory stricture, and 2) the presence or absence of an auditory-perceptual lateral and/or rhotic sound quality. Transcription and category frequencies for each speaker averaged across all environments were then compared with frequencies in specific phonological environments to ascertain whether a particular environment was amenable to a ‘drift’ towards any particular category of variant, and whether patterns of ‘drift’ applied to all speakers or varied on an individual basis. Transcriptions of the 1,535 tokens of /r/ ranged widely among lateral and non-lateral flaps, raised (i.e. increased articulatory contact) non-lateral flaps akin to light voiced plosives (e.g. Hattori 1951, Kawakami 1977), as well as lateral approximants and rhotic approximants. While two of the four speakers, both males, patterned similarly by dividing their productions of /r/ chiefly among short lateral approximants and rhotic approximants, each speaker did vary considerably in their choice of variants in any given environment. Drift is considered in terms of physiological parameters which may be optionally exploited to maintain phonological salience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".